Search Results (415)

This study investigates the socio-spatial distribution of food deserts in Santiago de Chile, aiming to understand how urban planning and socioeconomic factors influence access to nutritious food. Employing geospatial analysis techniques with data from OpenStreetMap and the 2017 Census, the research identifies areas within Santiago where access to healthy food is limited. The novelty of this study lies in its application of spatial autocorrelation methods, specifically Local Indicators of Spatial Association (LISA), to reveal clusters of nutritional inequality. The findings indicate significant concentrations of food deserts in both lower socioeconomic peripheral areas and, surprisingly, in some high-income central areas. These results suggest that both poverty and urban infrastructure, including car dependency, play critical roles in shaping access to healthy food. By highlighting over two million residents affected by food deserts, the study underscores the urgent need for integrated urban planning and public health strategies. This research contributes to the understanding of urban nutritional inequality and provides a replicable methodological framework for other cities. The implications extend beyond Santiago, offering insights into how urban design can be leveraged to improve public health outcomes through better access to nutritious food. Full article

Urban street space is a significant component of urban public spaces and an important aspect of people’s perceptions of a city. Jinan Old City exemplifies the balance between the supply of and demand for green spaces in urban streets. The sense of comfort and the demand level of street spaces are measured via the space demand index. Open platform data, such as those from Baidu Maps and Amap, are evaluated using methods including ArcGIS network analysis and Segnet semantic segmentation. The results obtained from such evaluations indicate that, in terms of the green space supply, the overall level for Shangxin Street in Jinan is not high. Only 24% of the selected sites have an adequate green space supply. The level on Wenhua West Road is higher than that on Shangxin Street. The block on the western side of Shangxin Street has the highest green space demand, with a decreasing trend from west to east. There are several higher selection points in the middle section of Shangxin Street. The demand is lowest in the middle of Wenhua East Road. Shangxin Street’s demand is higher than that of Wenhua West Road. The supply and demand are highly matched on Wenhua West Road and poorly matched on Shangxin Street, with 44.12% of the area in the “low supply, high demand” quadrant. This study proposes targeted optimization strategies based on supply and demand, thereby providing research ideas and methods for urban renewal. Full article

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) have changed the paradigm associated with the production of Geospatial Information (GI), since, due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is a topic that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in the literature about studies that analyse the completeness of OSM watercourses or even watercourses obtained by other sources. All this motivated the authors to develop a study that aims to analyse the completeness of these specific lines that have so much relevance to hydrologists. The study presents an analysis of the variation over time in completeness/coverage of the OSM “waterway” feature in the period between 2014 and 2023 in a mountainous region included in the Mondego River basin, located in the Inland of Portugal. The methodology applied is supported by classical methods of measuring the completeness of lines that may be found in the literature. The total length of the watercourses was calculated and compared in percentage terms with the total length of the reference watercourses for dates under analysis. The watercourses of the military official hydrography of the 1/25,000 scale were used as a reference. The relation of the OSM completeness with some indicators related to terrain surface (altitude, slope, and location/proximity settlements) was also analysed. The choice of these indicators was motivated by the fact that the study area has strong mountain characteristics and is crossed by the main Portuguese river. The analysis was performed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data and satellite image of Geographic Information System software. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of the studied period (nine years) and the fact that, nowadays, digital mobile devices enable easy access to satellite images, allowing the digitalization of geographic entities or objects of the real world remotely. Regarding the indicator altitude, slope, and location/proximity of the settlements, we believe that there is no influence of these indicators on the evolution of the completeness of the OSM waterways in the study area. Full article

This work aims to map the optimal routes based on time and distance, via e-scooters and walking, to visit 54 historical heritage sites in Faro. Implementing these routes promotes environmental sustainability by reducing CO₂ emissions and encouraging healthier, greener tourism. The route optimization was conducted in ArcGIS, utilizing the Network Analyst extension and vector data obtained from OpenStreetMap. The results showed that there are routes that can be completed in one or more days, depending on visitors’ availability, physical capacity, or their chosen method of transportation. The optimal route to visit the 54 historical heritage sites forms a closed circuit spanning 17.35 km. If visits are split into two routes, one covering 31 monuments in the old city and the other 24 monuments in the exterior area of the urban center, the optimal closed-circuit routes measure 6.16 km and 11.31 km, respectively. This study is expected to enhance tourism promoted by the Faro municipality and make it more environmentally friendly. Full article

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage. Full article

Exploring urban spatial structure plays an important role in promoting urban development, but there is a lack of research on the urban spatial structure of Xinjiang ports. This paper takes the central urban area of Kashi City as the study area and integrates points of interest (POI) data with nighttime light (NTL) data using the Open Street Map (OSM) road network to perform kernel density analysis, two-factor combination mapping, and partition identification. It identifies the spatial structural characteristics of the central urban area and divides it into different functional subdivisions. This research shows that ① the overall distributions of nighttime luminance values and POI kernel density are similar, and the overall distribution pattern gradually weakens from the city centre to the surrounding area. High-value areas are distributed in groups, presenting the spatial structure characteristics of one main area and two subareas. ② The fusion of POI data with OSM road network data identifies urban single functional zones and mixed functional zones and divides different functional zones in a more detailed way, with higher accuracy in identifying functional zones. ③ The coupling of POI and nighttime light remote sensing can better characterise the spatial features of the urban structure, such as large-scale homogeneous areas, urban fringe areas, suburbs and township centres, etc. The fusion of POI and the OSM road network can better characterise single and mixed land use types of urban land use and improve the part of the city that cannot be characterised by POI and night light. The results of this study are conducive to the realisation of rational and functional zoning in Kashi City and provide a reference for promoting urban human–land coordination and sustainable development. Full article

The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study’s results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility. Full article

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas. Full article

The functional structure of territorial space is an important factor for analyzing the interaction between humans and nature. However, the classification of remote sensing images struggles to distinguish between multiple functions provided by the same land use type. Therefore, we propose a framework to combine multi-source data for the recognition of dominant functions at the block level. Taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, its block-level ‘production–living–ecology’ functions were interpreted. The whole GBA was first divided into different blocks and its total, average, and proportional functional intensities were then calculated. Each block was labeled as a functional type considering the attributes of human activity and social information. The results show that the combination of land use/cover data, point of interest identification, and open street maps can efficiently separate the multiple and mixed functions of the same land use types. There is a great difference in the dominant functions of the cities in the GBA, and the spatial heterogeneity of their mixed functions is closely related to the development of their land resources and socio-economy. This provides a new perspective for recognizing the spatial structure of territorial space and can give important data for regulating and optimizing landscape patterns during sustainable development. Full article

The safety of cyclists on city streets is a significant concern, particularly with the rising number of accidents in densely populated areas. Urban environments present numerous challenges, such as complex road networks and heavy traffic, which increase the risk of cycling-related incidents. Such concern has been recurrent, even within smart city scenarios that have been focused on only expanding the cycling infrastructure. This article introduces an innovative low-cost embedded system designed to improve cycling safety in urban areas, taking geospatial data as input. By assessing the proximity to emergency services and utilizing GPS coordinates, the system can determine the indirect current risk level for cyclists, providing real-time alerts when crossing high-risk zones. Built on a Raspberry Pi Zero board, this solution is both cost-effective and efficient, making it easily reproducible in various urban settings. Preliminary results in Porto, Portugal, showcase the system’s practical application and effectiveness in enhancing cycling safety and supporting sustainable urban mobility. Full article

When confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the factors influencing urban carbon emissions and the practical strategies regarding urban vibrancy, the unclear mutual nexus between them and the development strategy for collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and buildings’ carbon emission data, we have developed a reproducible method to estimate total carbon emissions and emission intensity. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each of these four typologies based on the geographical location and requirements of urban development policies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects. Full article

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint. Full article

The educational problems in the area, economic disparities, conflict situations, and deficiencies in educational infrastructure directly affect the quality and accessibility of education. Therefore, the present research aims to generate comfort for users of the educational center by applying sustainable design strategies in Carabayllo, Peru. The study started with a literature review, an analysis of flora and fauna, passive design strategies, and climatic analysis applying sustainability strategies supported by digital tools (AutoCAD, Revit Collaborate, Climate Consultant, OpenStreetMap, JOSM, Rhinoceros, and Grasshopper). As a result, the design proposes an educational center that ensures year-round comfort through energy efficiency, the use of eco-friendly materials, and green roofs. Additionally, it includes the implementation of dry toilets, biofilters, and xerophytic vegetation for orchards, promoting food production and enhancing the treatment of nearby public spaces. In conclusion, this proposal enhances the quality of life for users by applying passive design strategies and sustainability principles, adopting clean energy sources, and efficiently managing waste, thereby contributing to the Sustainable Development Goals (SDGs). Full article

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. Full article

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images. Full article